Inherited genetic factors play an important role in pancreatic cancer risk with up to 10% of patients with pancreatic cancer reporting a family history of disease. Despite our past successes in identifying low-risk common pancreatic cancer susceptibility loci using genome-wide association approaches or high-penetrance genes using genomic sequencing. Much of the genetic basis of pancreatic cancer remains unexplained. Therefore, we hypothesize that by bringing together these two unique datasets (GWAS and whole-genome sequencing) we will be able to address some of the limitations of our previous analyses and identify novel pancreatic cancer susceptibility loci. Furthermore, leveraging our whole genome sequencing data we will be able to fine-map recently associated regions and develop a prioritized list of variants for future functional studies. We will accomplish these goal first conducting association analysis of whole genome sequencing data from 638 patients in 593 familial pancreatic cancer kindreds compared to 818 controls. We then use this genomic sequencing data to impute these variants into 9,220 pancreatic cancer patients and 12,567 controls followed by association analysis of the imputed data. We will also use this imputed data to fine-map previously established pancreatic cancer susceptibility loci. Candidate variants will then be directly genotyped in approximately 6,000 pancreatic cancer patients and 5,500 controls. We anticipate this work will identify novel pancreatic cancer susceptibility variants as well as identify putatively functional variants that may underlie some of the recently reported association signals. This will pave the way for identification of functional variants responsible fr these association signals and how environmental factors interact with the variants, which in turn will improve our understanding of the etiology of pancreatic cancer.